165 citations · 312 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 66 cited
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
Danial Amini, Ehsan Haghighat, Ruben Juanes
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We…
cs.LG2022★ 81 cited
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Danial Amini, Ehsan Haghighat, Ruben Juanes
Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE)…
cs.LG2021★ 165 cited
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Ehsan Haghighat, Danial Amini, Ruben Juanes
Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial dif…